Applying a Deep Learning Network in Continuous Physiological Parameter Estimation Based on Photoplethysmography Sensor Signals
Applying a Deep Learning Network in Continuous Physiological Parameter Estimation Based on Photoplethysmography Sensor Signals
复制标题
将深度学习网络应用于基于光电容积描记传感器信号的连续生理参数估计
DOI：
10.1109/jsen.2021.3126744
复制
发表时间：
2022
影响因子：
4.3
通讯作者：
Yi
中科院分区：
文献类型：
--
作者：
Chih;Jia;Yi
在本文中，我们提出了一个连续的生理参数估计模型的基础上，深度学习网络的光电容积脉搏波（PPG）传感器信号。在本研究中，将持续时间为8秒的信号并入所提出的模型中，用于频繁估计人体的收缩压（BP）、舒张压、心率（HR）和平均动脉压;这有助于早期识别和监测生理状况，从而降低心血管疾病的风险。该模型使用卷积神经网络（CNN）和长短期记忆（LSTM）网络设计。使用大规模重症监护多参数智能监测数据库对该模型进行训练和验证。CNN用于从PPG信号中自动提取特征。这种自动提取取代了传统的手工特征提取过程。然后使用LSTM网络分析具有时间序列的特征以估计生理参数。随后，进行了10倍交叉验证，结果显示参与者的收缩压、舒张压、心率和平均动脉压的平均绝对误差±标准差分别为2.54 ± 3.88、1.59 ± 2.45、1.62 ± 2.55和1.59 ± 2.34 mmHg。这些值符合医疗器械促进协会和英国高血压学会制定的标准。所提出的方法有利于准确，连续监测的BP和HR。
In this paper, we propose a continuous physiological parameter estimation model based on a deep learning network for photoplethysmography (PPG) sensor signals. Signals of 8-s duration were incorporated into the proposed model in this study for frequent estimation of the systolic blood pressure (BP), diastolic BP, heart rate (HR), and mean arterial pressure of the human body; this facilitated early identification and monitoring of physiological conditions and thus reduced the risk of cardiovascular disease. The proposed model was designed using a convolutional neural network (CNN) and long short-term memory (LSTM) network. This model was trained and validated using the large-scale Multiparameter Intelligent Monitoring in Intensive Care database. The CNN was used to extract features from PPG signals automatically. This automatic extraction replaced the conventional manual feature extraction process. Features with time-series were then analyzed using the LSTM network to estimate physiological parameters. Subsequently, ten-fold cross-validation was conducted to reveal the mean absolute errors ± standard deviations of participants’ systolic BP, diastolic BP, HR, and mean arterial pressure to be 2.54 ± 3.88, 1.59 ± 2.45, 1.62 ± 2.55, and 1.59 ± 2.34 mmHg, respectively. These values meet the standards established by the Association for the Advancement of Medical Instrumentation and the British Hypertension Society. The proposed method facilitates the accurate, continuous monitoring of the BP and HR.